This research studies the effects of both Daubechies wavelet basis function (DWBF) and decomposition level (DL) on the performance of detecting atrial fibrillation (AF) based on electrocardiograms (ECGs). ECG signals (consisting of 23 AF data and 18 normal data from MIT-BIH) were decomposed at various levels using several types of DWBF to obtain four wavelet coefficient features (WCFs), namely, minimum (min), maximum (max), mean, and standard deviation (stdev). These features were then classified to detect the presence of AF using a support vector machine (SVM) classifier. Distribution of training and testing data for the SVM uses the 5-fold cross-validation (CV) principle to produce optimum detection performance. In this study, AF detection performance is measured and analyzed based on accuracy, sensitivity, and specificity metrics. The results of the analysis show that accuracy tends to decrease with increases in the decomposition level. In addition, it becomes stable in various types of DWBF. For both sensitivity and specificity, the results of the analysis show that increasing the decomposition level also causes a decrease in both sensitivity and specificity. However, unlike the accuracy, changing the DWBF type causes both two metrics to fluctuate over a wider range. The statistical results also indicate that the highest AF accuracy detection (i.e., 94.17%) is obtained at the Daubechies 2 (DB₂) function with a decomposition level of 4, whereas the highest sensitivity, 97.57%, occurs when the AF detection uses DB₆ with a decomposition level of 2. Finally, DB₂ with decomposition level 4 results in 96.750% for specificity. The finding of this study is that selecting the appropriate DL has a more significant effect than DWBF on AF detection using WCF.

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

Background: To overcome the several drawbacks of warfarin, non-vitamin K antagonist oral anticoagulants (NOACs) were developed. Even though randomized controlled trials (RCTs) provided high-quality evidence, the real-world evidence is still needed. This systematic review and meta-analysis proposed to measure the safety and efficacy profile between warfarin and NOACs in non-valvular atrial fibrillation (NVAF) patients in preventing stroke.Results: We collected articles about the real-world studies comparing warfarin and NOACs for NVAF patients recorded in electronic scientific databases such as Embase, ProQuest, PubMed, and Cochrane. The pooled hazard ratio (HR) and 95% confidence interval (CI) were estimated using the generic inverse variance method. A total of 34 real-world studies, including 2287288 NVAF patients, were involved in this study. NOACs effectively reduced the stroke risk than warfarin (HR 0.77; 95% CI 0.69 to 0.87;p< 0.01). Moreover, NOACs effectively lowered all-cause mortality risk (HR 0.71; 95% CI 0.63 to 0.81;p< 0.01). From the safety aspect, compared to warfarin, NOACs significantly reduced major bleeding risk (HR 0.68; 95% CI 0.54 to 0.86;p< 0.01) and intracranial bleeding risk (HR 0.54; 95% CI 0.42 to 0.70;p< 0.01). However, NOACs administration failed to decrease gastrointestinal bleeding risk (HR 0.78; 95% CI 0.58 to 1.06;p= 0.12).Conclusions: In NVAF patients, NOACs were found to be more effective than warfarin at reducing stroke risk. NOACSs also lowered the risk of all-cause mortality, cerebral hemorrhage, and severe bleeding in NVAF patients compared to warfarin.

Arrhythmia, a heart rhythm disorder, remains a serious global health problem due to its potential to cause complications such as stroke and heart failure. Early detection and accurate classification of arrhythmia are crucial for appropriate medical intervention. Although deep learning holds promise for automatic arrhythmia detection using ECG signals, several challenges need to be addressed. Previous studies often utilize limited and imbalanced datasets and lack exploration of optimal pre-processing and feature extraction methods. To overcome these limitations, this study proposes the Optimized Cardiac Arrhythmia Detection Network (OCADN), a CNN-based model with hyperparameter optimization and advanced pre-processing techniques such as Discrete Wavelet Transform (DWT) and Z-score normalization. As a comparison to OCADN, this research also develops an arrhythmia detection model using the LSTM algorithm. Experimental results demonstrate that OCADN outperforms LSTM, achieving high accuracy, precision, sensitivity, specificity, and F1-score on both training and test data. The consistent performance of OCADN on both datasets indicates its robustness and potential for clinical implementation. OCADN with hyperparameter tuning exhibits accuracy, precision, sensitivity, specificity, and F1-score of 99.97%, 99.97%, 99.97%, 99.99%, and 99.97%, respectively, on the training data. Meanwhile, the performance on the testing data for accuracy, precision, sensitivity, specificity, and F1-score is 98.87%, 95.23%, 98.09%, 99.65%, and 96.59%, respectively.

Background Radiofrequency catheter ablation (RFCA) using the high-power short duration (HPSD) results in better ablation lesion formation in the swine model. This systematic review and meta-analysis purposed to investigate the safety and efficacy profile between HPSD and low-power long-duration (LPLD) ablation strategies to treat atrial fibrillation (AF) patients. Methods We completed the literature review after identifying the relevant articles comparing HPSD and LPLD ablation methods for AF recorded in ClinicalTrials.com, CENTRAL, PubMed, and ScienceDirect until February 2021. The overall effects were calculated using pooled risk ratio (RR) and mean difference (MD) for categorical and continuous data, respectively. We also estimated the 95% confidence interval (CI). Results The HPSD strategy took shorter procedure time (MD = −33.75 min; 95% CI = −44.54 to −22.97; P < .01), fluoroscopy time (MD = −5.73 min; 95% CI = −8.77 to −2.70; P < .001), and ablation time (MD = −17.71; 95% CI = −21.02 to −14.41) than LPLD strategy. The HPSD RFCA was correlated with lower risk of esophageal thermal injury (RR = 0.75; 95% CI = 0.59 to 0.94; P = .02). The HPSD method resulted in higher first-pass pulmonary vein isolation (PVI) (RR = 1.36; 95% CI = 1.13 to 1.64; P < .01), lower PV reconnection (RR = 0.47; 95% CI = 0.34 to 0.64; P < .01), and lower recurrent AF (RR = 0.72; 95% CI = 0.54 to 0.96; P = .02) than LPLD strategy. Conclusion HPSD RFCA was superior to the conventional LPLD RFCA in terms of safety and efficacy in treating AF patients.

The superiority of second-generation cryoballoon (2G-CB) ablation versus contact force-sensing radiofrequency (CF-RF) ablation in patients with paroxysmal atrial fibrillation (AF) was assessed in this systematic review and meta-analysis. Freedom from atrial tachyarrhythmias (ATAs) (OR = 0.89; 95% confidence interval [CI] = 0.68 to 1.17; p = 0.41), freedom from AF (OR = 0.93; 95% CI = 0.65 to 1.35; p = 0.72), and acute pulmonary vein isolation (PVI) (OR = 1.17; 95% CI = 0.54 to 2.53; p = 0.70) between 2G-CB ablation and CF-RF ablation were not different. The procedure time for the 2G-CB ablation was shorter (MD = −18.78 min; 95% CI = −27.72 to −9.85 min; p < 0.01), while the fluoroscopy time was similar (MD = 2.66 min; 95% CI = −0.52 to 5.83 min; p = 0.10). In the 2G-CB ablation group, phrenic nerve paralysis was more common (OR = 5.74; 95% CI = 1.80 to 18.31; p = < 0.01). Regarding freedom from ATAs, freedom from AF, and acute PVI, these findings imply that 2G-CB ablation is not superior to CF-RF ablation in paroxysmal AF. Although faster than CF-RF ablation, 2G-CB ablation has a greater risk of phrenic nerve paralysis.